mirror of
https://github.com/lllyasviel/ControlNet.git
synced 2026-01-10 22:47:57 -05:00
80 lines
4.0 KiB
Python
80 lines
4.0 KiB
Python
import cv2
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import einops
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import gradio as gr
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import numpy as np
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import torch
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from cldm.hack import disable_verbosity
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disable_verbosity()
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from pytorch_lightning import seed_everything
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from annotator.util import resize_image, HWC3
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from annotator.midas import apply_midas
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from cldm.model import create_model, load_state_dict
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from ldm.models.diffusion.ddim import DDIMSampler
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model = create_model('./models/cldm_v15.yaml').cuda()
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model.load_state_dict(load_state_dict('./models/control_sd15_depth.pth', location='cuda'))
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ddim_sampler = DDIMSampler(model)
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def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, scale, seed, eta):
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with torch.no_grad():
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input_image = HWC3(input_image)
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detected_map, _ = apply_midas(resize_image(input_image, detect_resolution))
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detected_map = HWC3(detected_map)
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img = resize_image(input_image, image_resolution)
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H, W, C = img.shape
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detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
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control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
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control = torch.stack([control for _ in range(num_samples)], dim=0)
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control = einops.rearrange(control, 'b h w c -> b c h w').clone()
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seed_everything(seed)
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cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
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un_cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
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shape = (4, H // 8, W // 8)
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samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
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shape, cond, verbose=False, eta=eta,
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unconditional_guidance_scale=scale,
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unconditional_conditioning=un_cond)
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x_samples = model.decode_first_stage(samples)
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x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
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results = [x_samples[i] for i in range(num_samples)]
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return [detected_map] + results
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block = gr.Blocks().queue()
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with block:
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with gr.Row():
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gr.Markdown("## Control Stable Diffusion with Depth Maps")
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with gr.Row():
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with gr.Column():
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input_image = gr.Image(source='upload', type="numpy")
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prompt = gr.Textbox(label="Prompt")
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run_button = gr.Button(label="Run")
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with gr.Accordion("Advanced options", open=False):
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num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
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image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=256)
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detect_resolution = gr.Slider(label="Depth Resolution", minimum=128, maximum=1024, value=384, step=1)
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ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
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scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
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seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True)
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eta = gr.Number(label="eta (DDIM)", value=0.0)
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a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
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n_prompt = gr.Textbox(label="Negative Prompt",
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value='longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality')
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with gr.Column():
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result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
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ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, scale, seed, eta]
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run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
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block.launch(server_name='0.0.0.0')
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